Agent Skills › langchain-ai/langchain-skills › langgraph-python-quickstart

langgraph-python-quickstart

GitHub

指导用户基于 LangGraph 官方文档，在本地快速搭建最小化的 Python Agent。涵盖环境初始化、模型配置及基础代码实现，确保轻量且支持多模型提供商。

config/skills/langgraph-python-quickstart/SKILL.md langchain-ai/langchain-skills

Trigger Scenarios

用户希望快速尝试或构建 LangGraph Agent 需要在本地环境中从零开始设置 LangGraph 项目

Install

npx skills add langchain-ai/langchain-skills --skill langgraph-python-quickstart -g -y

More Options

Non-standard path

npx skills add https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/langgraph-python-quickstart -g -y

Use without installing

npx skills use langchain-ai/langchain-skills@langgraph-python-quickstart

指定 Agent (Claude Code)

npx skills add langchain-ai/langchain-skills --skill langgraph-python-quickstart -a claude-code -g -y

安装 repo 全部 skill

npx skills add langchain-ai/langchain-skills --all -g -y

预览 repo 内 skill

npx skills add langchain-ai/langchain-skills --list

SKILL.md

Frontmatter

{
    "name": "langgraph-python-quickstart",
    "description": "Scaffold a minimal local LangGraph agent in Python by following the official quickstart. Use when the user wants to quickly build or try a LangGraph agent locally."
}

LangGraph Python quickstart

Follow the live docs — do not invent an alternate API from memory:

https://docs.langchain.com/oss/python/langgraph/quickstart

Fetch that page (Docs MCP or HTTP) and implement what it shows (calculator / math agent with the Graph API). Prefer the Graph API path over the Functional API unless the user asks otherwise. Skip IPython graph visualization.

Local setup constraints

Apply these on top of the quickstart (they keep setup minimal and model-agnostic):

Ask which provider/model to use. Showcase that LangGraph works with any LangChain chat model. Suggested prompt:

Which model should this agent use? Pass a provider:model string — e.g. openai:gpt-5.5, anthropic:claude-sonnet-5, google_genai:gemini-2.5-flash-lite. Default if you're unsure: anthropic:claude-sonnet-5.

The docs often hardcode Anthropic — replace with init_chat_model("<MODEL>") (or equivalent) using their choice. If using Claude Sonnet 5+, omit temperature / top_p / top_k (unsupported).
Create a new directory (e.g. langgraph-agent/) and do all work there — do not pollute the open project.
Only secret: the provider API key in .env (gitignored). No LangSmith / Tavily unless they ask. Prefer they edit .env themselves — don't paste keys into chat.
Install packages from the quickstart plus the provider package for their model.
Run the example (e.g. “Add 3 and 4.”), show output, then stop. Point to langgraph-fundamentals for next steps. For a higher-level agent API, use LangChain create_agent instead.